17,020 research outputs found
Local Binary Patterns on Hexagonal Image Structure
Local binary pattern (LBP) was designed and widely used for efficient texture classification. It has been used for face recognition and has potential applications in many other research areas such as human detection. LBP provides a simple and effective way to represent patterns. Uniform LBPs play an important role for LBP-based pattern /object recognition as they include majority of LBPs. In this paper, we present LBP codes on hexagonal image structure. We show that LBPs defined on hexagonal structure have higher percentages of uniform LBPs that will lead to a more efficient and accurate recognition scheme for image classification
Quasi-two-dimensional complex plasma containing spherical particles and their binary agglomerates
A new type of quasi-two-dimensional complex plasma system was observed which
consisted of monodisperse microspheres and their binary agglomerations
(dimers). The particles and their dimers levitated in a plasma sheath at
slightly different heights and formed two distinct sublayers. The sys- tem did
not crystallize and may be characterized as disordered solid. The dimers were
identified based on their characteristic appearance in defocused images, i.e.,
rotating interference fringe pat- terns. The in-plane and inter-plane particle
separations exhibit nonmonotonic dependence on the discharge pressure which
agrees well with theoretical predictions
Extended patchy ecosystems may increase their total biomass through self-replication
Patches of vegetation consist of dense clusters of shrubs, grass, or trees,
often found to be circular characteristic size, defined by the properties of
the vegetation and terrain. Therefore, vegetation patches can be interpreted as
localized structures. Previous findings have shown that such localized
structures can self-replicate in a binary fashion, where a single vegetation
patch elongates and divides into two new patches. Here, we extend these
previous results by considering the more general case, where the plants
interact non-locally, this extension adds an extra level of complexity and
shrinks the gap between the model and real ecosystems, where it is known that
the plant-to-plant competition through roots and above-ground facilitating
interactions have non-local effects, i.e. they extend further away than the
nearest neighbor distance. Through numerical simulations, we show that for a
moderate level of aridity, a transition from a single patch to periodic pattern
occurs. Moreover, for large values of the hydric stress, we predict an opposing
route to the formation of periodic patterns, where a homogeneous cover of
vegetation may decay to spot-like patterns. The evolution of the biomass of
vegetation patches can be used as an indicator of the state of an ecosystem,
this allows to distinguish if a system is in a self-replicating or decaying
dynamics. In an attempt to relate the theoretical predictions to real
ecosystems, we analyze landscapes in Zambia and Mozambique, where vegetation
forms patches of tens of meters in diameter. We show that the properties of the
patches together with their spatial distributions are consistent with the
self-organization hypothesis. We argue that the characteristics of the observed
landscapes may be a consequence of patch self-replication, however, detailed
field and temporal data is fundamental to assess the real state of the
ecosystems.Comment: 38 pages, 12 figures, 1 tabl
In situ visualization of Ni-Nb bulk metallic glasses phase transition
We report the results of the Ni-based bulk metallic glass structural
evolution and crystallization behavior in situ investigation. The X-ray
diffraction (XRD), transmission electron microscopy (TEM), nano-beam
diffraction (NBD), differential scanning calorimetry (DSC), radial distribution
function (RDF) and scanning probe microscopy/spectroscopy (STM/STS) techniques
were applied to analyze the structure and electronic properties of Ni63.5Nb36.5
glasses before and after crystallization. It was proved that partial surface
crystallization of Ni63.5Nb36.5 can occur at the temperature lower than for the
full sample crystallization. According to our STM measurements the primary
crystallization is originally starting with the Ni3Nb phase formation. It was
shown that surface crystallization drastically differs from the bulk
crystallization due to the possible surface reconstruction. The mechanism of
Ni63.5Nb36.5 glass alloy 2D-crystallization was suggested, which corresponds to
the local metastable (3x3)-Ni(111) surface phase formation. The possibility of
different surface nano-structures development by the annealing of the
originally glassy alloy in ultra high vacuum at the temperature lower, than the
crystallization temperature was shown. The increase of mean square surface
roughness parameter Rq while moving from glassy to fully crystallized state can
be caused by concurrent growth of Ni3Nb and Ni6Nb7 bulk phases. The simple
empirical model for the estimation of Ni63.5Nb36.5 cluster size was suggested,
and the obtained values (7.64 A, 8.08 A) are in good agreement with STM
measurements data (8 A-10 A)
Direct Evidence for the Source of Reported Magnetic Behavior in "CoTe"
In order to unambiguously identify the source of magnetism reported in recent
studies of the Co-Te system, two sets of high-quality, epitaxial CoTe films
(thickness 300 nm) were prepared by pulse laser deposition (PLD).
X-ray diffraction (XRD) shows that all of the films are epitaxial along the
[001] direction and have the hexagonal NiAs structure. There is no indication
of any second phase metallic Co peaks (either or ) in the XRD
patterns. The two sets of CoTe films were grown on various substrates with
PLD targets having Co:Te in the atomic ratio of 50:50 and 35:65. From the
measured lattice parameters for the former and
for the latter, the compositions CoTe (63.1% Te) and CoTe
(63.8% Te), respectively, are assigned to the principal phase. Although XRD
shows no trace of metallic Co second phase, the magnetic measurements do show a
ferromagnetic contribution for both sets of films with the saturation
magnetization values for the CoTe films being approximately four times
the values for the CoTe films. Co spin-echo nuclear magnetic
resonance (NMR) clearly shows the existence of metallic Co inclusions in the
films. The source of weak ferromagnetism reported in several recent studies is
due to the presence of metallic Co, since the stoichiometric composition "CoTe"
does not exist.Comment: 19 pages, 7 figure
Large tunable valley splitting in edge-free graphene quantum dots on boron nitride
Coherent manipulation of binary degrees of freedom is at the heart of modern
quantum technologies. Graphene offers two binary degrees: the electron spin and
the valley. Efficient spin control has been demonstrated in many solid state
systems, while exploitation of the valley has only recently been started, yet
without control on the single electron level. Here, we show that van-der Waals
stacking of graphene onto hexagonal boron nitride offers a natural platform for
valley control. We use a graphene quantum dot induced by the tip of a scanning
tunneling microscope and demonstrate valley splitting that is tunable from -5
to +10 meV (including valley inversion) by sub-10-nm displacements of the
quantum dot position. This boosts the range of controlled valley splitting by
about one order of magnitude. The tunable inversion of spin and valley states
should enable coherent superposition of these degrees of freedom as a first
step towards graphene-based qubits
Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data
This document is the Accepted Manuscript version of the following article: Emmanuel Oluwatobi Salawu, Evelyn Hesse, Chris Stopford, Neil Davey, and Yi Sun, 'Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data', Journal of Quantitative Spectroscopy and Radiative Transfer, Vol. 201, pp. 115-127, first published online 5 July 2017. Under embargo. Embargo end date: 5 July 2019. The Version of Record is available online at doi: https://doi.org/10.1016/j.jqsrt.2017.07.001. © 2017 Elsevier Ltd. All rights reserved.Better understanding and characterization of cloud particles, whose properties and distributions affect climate and weather, are essential for the understanding of present climate and climate change. Since imaging cloud probes have limitations of optical resolution, especially for small particles (with diameter < 25 μm), instruments like the Small Ice Detector (SID) probes, which capture high-resolution spatial light scattering patterns from individual particles down to 1 μm in size, have been developed. In this work, we have proposed a method using Machine Learning techniques to estimate simulated particles’ orientation-averaged projected sizes (PAD) and aspect ratio from their 2D scattering patterns. The two-dimensional light scattering patterns (2DLSP) of hexagonal prisms are computed using the Ray Tracing with Diffraction on Facets (RTDF) model. The 2DLSP cover the same angular range as the SID probes. We generated 2DLSP for 162 hexagonal prisms at 133 orientations for each. In a first step, the 2DLSP were transformed into rotation-invariant Zernike moments (ZMs), which are particularly suitable for analyses of pattern symmetry. Then we used ZMs, summed intensities, and root mean square contrast as inputs to the advanced Machine Learning methods. We created one random forests classifier for predicting prism orientation, 133 orientation-specific (OS) support vector classification models for predicting the prism aspect-ratios, 133 OS support vector regression models for estimating prism sizes, and another 133 OS Support Vector Regression (SVR) models for estimating the size PADs. We have achieved a high accuracy of 0.99 in predicting prism aspect ratios, and a low value of normalized mean square error of 0.004 for estimating the particle’s size and size PADs.Peer reviewe
A minimal statistical-mechanical model for multihyperuniform patterns in avian retina
Birds are known for their extremely acute sense of vision. The very peculiar
structural distribution of five different types of cones in the retina
underlies this exquisite ability to sample light. It was recently found that
each cone population as well as their total population display a disordered
pattern in which long wave-length density fluctuations vanish. This property,
known as hyperuniformity is also present in perfect crystals. In situations
like the avian retina in which both the global structure and that of each
component display hyperuniformity, the system is said to be multi-hyperuniform.
In this work, we aim at devising a minimal statistical-mechanical model that
can reproduce the main features of the spatial distribution of photoreceptors
in avian retina, namely the presence of disorder, multi-hyperuniformity and
local hetero-coordination. This last feature is key to avoid local clustering
of the same type of photoreceptors, an undesirable feature for the efficient
sampling of light. For this purpose we formulate a simple model that
definitively exhibits the required structural properties, namely an equimolar
three-component mixture (one component to sample each primary color, red,
green, and blue) of non-additive hard disks to which a long-range logarithmic
repulsion is added between like particles. A Voronoi analysis of our idealized
system of photoreceptors shows that the space-filling Voronoi polygons
interestingly display a rather uniform area distribution, symmetrically
centered around that of a regular lattice, a structural property also found in
human retina. Disordered multi-hyperuniformity offers an alternative to
generate photoreceptor patterns with minimal long-range concentration and
density fluctuations. This is the key to overcome the difficulties in devising
an efficient visual system in which crystal-like order is absent
Polycrystalline graphene and other two-dimensional materials
Graphene, a single atomic layer of graphitic carbon, has attracted intense
attention due to its extraordinary properties that make it a suitable material
for a wide range of technological applications. Large-area graphene films,
which are necessary for industrial applications, are typically polycrystalline,
that is, composed of single-crystalline grains of varying orientation joined by
grain boundaries. Here, we present a review of the large body of research
reported in the past few years on polycrystalline graphene. We discuss its
growth and formation, the microscopic structure of grain boundaries and their
relations to other types of topological defects such as dislocations. The
review further covers electronic transport, optical and mechanical properties
pertaining to the characterizations of grain boundaries, and applications of
polycrystalline graphene. We also discuss research, still in its infancy,
performed on other 2D materials such as transition metal dichalcogenides, and
offer perspectives for future directions of research.Comment: review article; part of focus issue "Graphene applications
On buoyant convection in binary solidification
We consider the problem of nonlinear steady buoyant convection in horizontal mushy layers during the solidification of binary alloys. We investigate both cases of zero vertical volume flux and constant pressure, referred to as impermeable and permeable conditions, respectively, at the upper mush???liquid interface. We analyze the effects of several parameters of the problem on the stationary modes of convection in the form of either hexagonal cells or non-hexagonal cells, such as rolls, rectangles and squares. [More ...]published or submitted for publicationis not peer reviewe
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